The present invention relates generally to expert systems, and, more particularly, to a computerized method and system for making decisions based on evidential reasoning, such as may be used for making decisions regarding risk and credit analysis for financial service applications.
Evidential reasoning is an artificial intelligence methodology that generally starts with a hierarchical description of a decision process used in a particular field, such as business, engineering, medical diagnostics, etc. The hierarchical description is used to develop a model structure represented by a plurality of processing nodes. Each node in the model structure represents an intermediate or final consideration and opinion used in the decision process. Each node contains a number of attributes describing factors to be considered for that node. Each attribute has a number of possible linguistic evidential values. The linguistic evidential values are converted to numeric evidential values at the nodes. The numeric evidential values express a degree to which the linguistic evidential values support a particular hypothesis for the attributes. For example, there can be a high belief, a medium belief, or a low belief that the linguistic evidential values support the hypothesis. The numeric evidential values for all of the attributes in a node are combined and used to formulate an opinion for the node. The opinion from each node is then propagated to the next higher level node where it becomes the linguistic evidential value for the appropriate attribute in that higher level node. The linguistic evidential values at the higher level nodes are then converted to numeric evidential values and combined at the nodes to formulate additional opinions. This process continues until a final opinion is formulated at the highest level node in the model structure.
The combination of the numeric evidential values at the nodes to formulate an opinion may be accomplished by using a non-linear algorithm. The Mycin function is one type of non-linear algorithm that has been used to combine numeric evidential values. The Mycin function resembles a special case in the Dempster-Schaffer Theory of Evidence. The Mycin function is adapted from the certainty theory work formulated by Shortliffe et al., A Model of Inexact Reasoning in Medicine. See Chapter 11 in Buchanan & Shortliffe, Rule-Based Expert Systems: The Mycin Experiments Of The Stanford Heuristic Programming Project, Addison-Wesley, MA, 1985.
One area of interest to the assignee of the present invention is the ability to participate in electronic commerce (eCommerce) business ventures by offering financial services over a global communications network such as the Internet. It is believed that one key consideration to succeed in this area is the ability to systematically and reliably estimate the financial risk involved in any given offering prior to committing resources to that offering. Another consideration is to quickly make any such estimates and make appropriate decisions with minimal human intervention, such as may be implemented with a computerized system. In particular, it would be desirable to offer financial services associated with electronic business-to-business (B2B) transactions through a global communications network. As suggested above, one key element in this strategy is the ability to quickly and inexpensively yet systematically and reliably evaluate risk and assign appropriate lines of credit. Thus, it would be desirable to provide computerized techniques for developing a comprehensive, quantitative underwriting model and risk rating methodology that can be used over a global communications network to evaluate credit requests and assign credit lines.
Modeling approaches may differ depending on the complexity of the decision to be made and the amount of good historical data available. For example, if there is access to large volumes of good quality historical data that characterize good and bad credit risks, then models are typically developed using statistical regression, neural nets, data mining or other mathematical techniques that analyze large data sets. Model development in the absence of data, however, typically requires advanced analytic techniques to evaluate and manage information in order to make strategic decisions or recommendations. In these situations, one key objective is to gather enough information and evidence in support of a final decision or rating. As will be appreciated by those skilled in the art, the computerized analysis of credit request information is a challenging activity, since it requires emulating the thought process of expert analysts, and such analysis typically involves the use of judgement in aggregating facts or evidence about a particular situation. For credit decisions, evidence indicating the financial strength, company quality, payment history, credit agency ratings, etc. are combined to determine an appropriate line of credit. See U.S. patent application Ser. No. 09/820,675 (RD-28,220), titled “Computerized Method For Determining A Credit Line” and filed concurrently herewith, for background information regarding an innovative technique that allows to quickly and systematically determine a credit line to be issued by a financial service provider to any given business applicant entity.
The act of forming a judgement involves balancing countervailing factors to arrive at a decision. Judgement involves not just culling out the obviously bad cases or accepting the obviously good cases, but making the proper decision in the gray area in the middle. In general, the weight or importance of a particular piece of evidence is not fixed but is dependent on the values of the other items being aggregated. U.S. Pat. No. 5,832,465, commonly assigned to the assignee of the present invention, discloses a technique for building a self-learning evidentiary reasoning system that facilitates the capture of the experts thought processes and encapsulate them in an computer-based model configured to give expert advise. The present invention further improves the foregoing technique to enable automated decision making, particularly, in situations when there are missing pieces of evidence.